Overview

Dataset statistics

Number of variables14
Number of observations52704
Missing cells7666
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
blade_angle has 707 (1.3%) missing valuesMissing
Rear bearing temperature (°C) has 707 (1.3%) missing valuesMissing
Nacelle ambient temperature (°C) has 707 (1.3%) missing valuesMissing
Front bearing temperature (°C) has 707 (1.3%) missing valuesMissing
Tower Acceleration X (mm/ss) has 706 (1.3%) missing valuesMissing
Tower Acceleration y (mm/ss) has 706 (1.3%) missing valuesMissing
Metal particle count counter has 707 (1.3%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 16956 (32.2%) zerosZeros
Rotor speed (RPM) has 1334 (2.5%) zerosZeros

Reproduction

Analysis started2023-07-08 11:59:25.914535
Analysis finished2023-07-08 11:59:43.293565
Duration17.38 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52704
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size411.9 KiB
Minimum2020-01-01 00:00:00
Maximum2020-12-31 23:50:00
2023-07-08T17:29:43.344357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:43.560951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52204
Distinct (%)99.9%
Missing454
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean604.86699
Minimum-16.713042
Maximum2075.3792
Zeros3
Zeros (%)< 0.1%
Negative6193
Negative (%)11.8%
Memory size411.9 KiB
2023-07-08T17:29:43.663550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16.713042
5-th percentile-2.107325
Q1102.83849
median358.7491
Q3938.94716
95-th percentile1988.4944
Maximum2075.3792
Range2092.0923
Interquartile range (IQR)836.10867

Descriptive statistics

Standard deviation632.9563
Coefficient of variation (CV)1.0464388
Kurtosis-0.18723593
Mean604.86699
Median Absolute Deviation (MAD)321.40535
Skewness1.0377873
Sum31604300
Variance400633.67
MonotonicityNot monotonic
2023-07-08T17:29:43.759069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
< 0.1%
-1.120350027 3
 
< 0.1%
-1.539681041 3
 
< 0.1%
-1.677857536 2
 
< 0.1%
-1.446318537 2
 
< 0.1%
911.9582275 2
 
< 0.1%
-1.742944556 2
 
< 0.1%
-1.015250528 2
 
< 0.1%
-1.099543515 2
 
< 0.1%
-2.296717536 2
 
< 0.1%
Other values (52194) 52227
99.1%
(Missing) 454
 
0.9%
ValueCountFrequency (%)
-16.71304195 1
< 0.1%
-16.14801345 1
< 0.1%
-15.20577469 1
< 0.1%
-14.79755473 1
< 0.1%
-14.77412 1
< 0.1%
-14.57791103 1
< 0.1%
-14.39000955 1
< 0.1%
-14.33189291 1
< 0.1%
-14.31416845 1
< 0.1%
-13.89057263 1
< 0.1%
ValueCountFrequency (%)
2075.379227 1
< 0.1%
2073.743488 1
< 0.1%
2073.024274 1
< 0.1%
2072.596417 1
< 0.1%
2071.641882 1
< 0.1%
2071.306836 1
< 0.1%
2071.141992 1
< 0.1%
2070.468127 1
< 0.1%
2070.452148 1
< 0.1%
2070.114429 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52250
Distinct (%)> 99.9%
Missing453
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean197.68874
Minimum0.036441628
Maximum359.99584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:43.857174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.036441628
5-th percentile27.3549
Q1144.53023
median215.72271
Q3257.58476
95-th percentile327.96827
Maximum359.99584
Range359.95939
Interquartile range (IQR)113.05452

Descriptive statistics

Standard deviation92.057052
Coefficient of variation (CV)0.46566664
Kurtosis-0.63721652
Mean197.68874
Median Absolute Deviation (MAD)50.030031
Skewness-0.5499055
Sum10329434
Variance8474.5008
MonotonicityNot monotonic
2023-07-08T17:29:43.956921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.9100037 2
 
< 0.1%
114.4201504 1
 
< 0.1%
232.6615583 1
 
< 0.1%
175.7968131 1
 
< 0.1%
37.5028799 1
 
< 0.1%
113.1955998 1
 
< 0.1%
92.31299943 1
 
< 0.1%
67.87381253 1
 
< 0.1%
29.26002347 1
 
< 0.1%
23.51287505 1
 
< 0.1%
Other values (52240) 52240
99.1%
(Missing) 453
 
0.9%
ValueCountFrequency (%)
0.0364416282 1
< 0.1%
0.05492279161 1
< 0.1%
0.06014951032 1
< 0.1%
0.06350191812 1
< 0.1%
0.06753250976 1
< 0.1%
0.09202225884 1
< 0.1%
0.09617212228 1
< 0.1%
0.1022138833 1
< 0.1%
0.1305246988 1
< 0.1%
0.1362498996 1
< 0.1%
ValueCountFrequency (%)
359.9958364 1
< 0.1%
359.9727056 1
< 0.1%
359.9690886 1
< 0.1%
359.9660308 1
< 0.1%
359.9452785 1
< 0.1%
359.921572 1
< 0.1%
359.8818561 1
< 0.1%
359.8657647 1
< 0.1%
359.8610112 1
< 0.1%
359.8453308 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14630
Distinct (%)28.0%
Missing453
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean198.05244
Minimum0.21554637
Maximum359.91331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:44.063579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.21554637
5-th percentile27.655342
Q1147.28864
median216.43457
Q3258.14267
95-th percentile328.38702
Maximum359.91331
Range359.69776
Interquartile range (IQR)110.85403

Descriptive statistics

Standard deviation92.267273
Coefficient of variation (CV)0.46587295
Kurtosis-0.62267211
Mean198.05244
Median Absolute Deviation (MAD)49.390442
Skewness-0.56008034
Sum10348438
Variance8513.2497
MonotonicityNot monotonic
2023-07-08T17:29:44.164827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246.069458 129
 
0.2%
252.6548462 126
 
0.2%
188.9954529 123
 
0.2%
182.4100952 121
 
0.2%
252.6542664 118
 
0.2%
249.3621216 115
 
0.2%
221.923584 114
 
0.2%
214.2400818 112
 
0.2%
219.7279053 110
 
0.2%
257.0450745 108
 
0.2%
Other values (14620) 51075
96.9%
(Missing) 453
 
0.9%
ValueCountFrequency (%)
0.2155463696 3
 
< 0.1%
0.2155761719 1
 
< 0.1%
0.2156066895 1
 
< 0.1%
0.2156074047 11
< 0.1%
0.2156684399 2
 
< 0.1%
0.2161560059 7
< 0.1%
0.2162475586 4
 
< 0.1%
0.2162482738 1
 
< 0.1%
0.2163341045 1
 
< 0.1%
0.2163951397 5
< 0.1%
ValueCountFrequency (%)
359.9133072 1
< 0.1%
359.7925295 1
< 0.1%
359.7387877 1
< 0.1%
359.730537 1
< 0.1%
359.3881073 1
< 0.1%
359.3625425 1
< 0.1%
359.2939866 1
< 0.1%
359.2655034 1
< 0.1%
359.2066909 1
< 0.1%
359.1191101 1
< 0.1%

blade_angle
Real number (ℝ)

MISSING  ZEROS 

Distinct23091
Distinct (%)44.4%
Missing707
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean6.6045988
Minimum0
Maximum93.22631
Zeros16956
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:44.272428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.26035088
Q31.4933333
95-th percentile44.993334
Maximum93.22631
Range93.22631
Interquartile range (IQR)1.4933333

Descriptive statistics

Standard deviation16.1893
Coefficient of variation (CV)2.4512163
Kurtosis9.8776628
Mean6.6045988
Median Absolute Deviation (MAD)0.26035088
Skewness3.0648486
Sum343419.32
Variance262.09345
MonotonicityNot monotonic
2023-07-08T17:29:44.367766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16956
32.2%
44.99333445 3511
 
6.7%
1.49333334 888
 
1.7%
0.02466666698 626
 
1.2%
44.99666723 341
 
0.6%
89.99333191 312
 
0.6%
1.49333334 246
 
0.5%
0.04933333397 235
 
0.4%
1.49666667 214
 
0.4%
92.47333527 165
 
0.3%
Other values (23081) 28503
54.1%
(Missing) 707
 
1.3%
ValueCountFrequency (%)
0 16956
32.2%
0.0001587301552 1
 
< 0.1%
0.0001666666622 6
 
< 0.1%
0.0001666666629 8
 
< 0.1%
0.0002083333287 1
 
< 0.1%
0.0003333333201 3
 
< 0.1%
0.0003333333244 12
 
< 0.1%
0.0003333333259 12
 
< 0.1%
0.0003508771836 1
 
< 0.1%
0.0003508771851 4
 
< 0.1%
ValueCountFrequency (%)
93.22631043 1
 
< 0.1%
92.49333191 39
 
0.1%
92.49333191 16
 
< 0.1%
92.48333232 2
 
< 0.1%
92.47333527 1
 
< 0.1%
92.47333527 4
 
< 0.1%
92.47333527 165
0.3%
92.46999868 1
 
< 0.1%
92.46999868 1
 
< 0.1%
92.46999868 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39184
Distinct (%)75.4%
Missing707
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean63.822108
Minimum14.928947
Maximum76.590001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:44.465489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.928947
5-th percentile42.482
Q162.74
median67.07
Q369.0275
95-th percentile71.014999
Maximum76.590001
Range61.661054
Interquartile range (IQR)6.2875002

Descriptive statistics

Standard deviation8.9668471
Coefficient of variation (CV)0.14049751
Kurtosis5.2424785
Mean63.822108
Median Absolute Deviation (MAD)2.4950005
Skewness-2.2641406
Sum3318558.1
Variance80.404347
MonotonicityNot monotonic
2023-07-08T17:29:44.559339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.79999924 16
 
< 0.1%
69.29499931 8
 
< 0.1%
67.75999985 8
 
< 0.1%
67.49500008 8
 
< 0.1%
69.4875 8
 
< 0.1%
68.13749962 8
 
< 0.1%
69.45999985 8
 
< 0.1%
69.47499962 8
 
< 0.1%
69.50249977 8
 
< 0.1%
70.32749977 8
 
< 0.1%
Other values (39174) 51909
98.5%
(Missing) 707
 
1.3%
ValueCountFrequency (%)
14.9289471 1
< 0.1%
15.02000008 1
< 0.1%
15.2275001 1
< 0.1%
15.25000014 1
< 0.1%
15.45882337 1
< 0.1%
15.52307701 1
< 0.1%
15.54285731 1
< 0.1%
15.60000038 2
< 0.1%
15.72999997 1
< 0.1%
15.76500006 1
< 0.1%
ValueCountFrequency (%)
76.5900013 1
< 0.1%
76.26000023 1
< 0.1%
74.92894665 1
< 0.1%
74.6899971 1
< 0.1%
74.68157477 1
< 0.1%
74.64999746 1
< 0.1%
74.6399971 1
< 0.1%
74.60263182 1
< 0.1%
74.53999939 1
< 0.1%
74.51499863 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50762
Distinct (%)97.2%
Missing453
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10.429403
Minimum0
Maximum15.336061
Zeros1334
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:44.659574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.51497434
Q19.1793314
median10.512142
Q313.828083
95-th percentile15.157686
Maximum15.336061
Range15.336061
Interquartile range (IQR)4.6487516

Descriptive statistics

Standard deviation4.1206878
Coefficient of variation (CV)0.39510296
Kurtosis0.84810329
Mean10.429403
Median Absolute Deviation (MAD)1.6581974
Skewness-1.1227815
Sum544946.71
Variance16.980068
MonotonicityNot monotonic
2023-07-08T17:29:44.762299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1334
 
2.5%
8.989999771 33
 
0.1%
0.01050000242 12
 
< 0.1%
0.0110000018 10
 
< 0.1%
0.01200000197 8
 
< 0.1%
0.01150000188 7
 
< 0.1%
0.01300000213 6
 
< 0.1%
9.569999695 5
 
< 0.1%
0.01400000229 5
 
< 0.1%
9.25 4
 
< 0.1%
Other values (50752) 50827
96.4%
(Missing) 453
 
0.9%
ValueCountFrequency (%)
0 1334
2.5%
0.002719500626 1
 
< 0.1%
0.008594118168 1
 
< 0.1%
0.009999999776 1
 
< 0.1%
0.01050000242 12
 
< 0.1%
0.0110000018 10
 
< 0.1%
0.01105263413 1
 
< 0.1%
0.01130850264 1
 
< 0.1%
0.01150000188 7
 
< 0.1%
0.01157894927 1
 
< 0.1%
ValueCountFrequency (%)
15.33606072 1
< 0.1%
15.31956576 1
< 0.1%
15.31214578 1
< 0.1%
15.30648968 1
< 0.1%
15.29546334 1
< 0.1%
15.29402791 1
< 0.1%
15.29278766 1
< 0.1%
15.29216637 1
< 0.1%
15.29055655 1
< 0.1%
15.28538664 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52235
Distinct (%)> 99.9%
Missing453
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1234.7748
Minimum-583.41969
Maximum1819.3284
Zeros5
Zeros (%)< 0.1%
Negative594
Negative (%)1.1%
Memory size411.9 KiB
2023-07-08T17:29:44.869653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-583.41969
5-th percentile60.276961
Q11089.5433
median1247.1958
Q31638.8783
95-th percentile1796.1648
Maximum1819.3284
Range2402.7481
Interquartile range (IQR)549.33499

Descriptive statistics

Standard deviation494.31627
Coefficient of variation (CV)0.4003291
Kurtosis1.1327938
Mean1234.7748
Median Absolute Deviation (MAD)195.97025
Skewness-1.1895123
Sum64518217
Variance244348.58
MonotonicityNot monotonic
2023-07-08T17:29:44.966465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
< 0.1%
1070.869995 4
 
< 0.1%
1070.859985 3
 
< 0.1%
1071.02002 3
 
< 0.1%
1066.885637 2
 
< 0.1%
1070.77002 2
 
< 0.1%
1070.829956 2
 
< 0.1%
1139.459961 2
 
< 0.1%
1283.123594 2
 
< 0.1%
75.32678354 1
 
< 0.1%
Other values (52225) 52225
99.1%
(Missing) 453
 
0.9%
ValueCountFrequency (%)
-583.4196854 1
< 0.1%
-578.1824153 1
< 0.1%
-578.1802877 1
< 0.1%
-578.1767464 1
< 0.1%
-578.1749086 1
< 0.1%
-578.1747256 1
< 0.1%
-578.1744289 1
< 0.1%
-578.1706953 1
< 0.1%
-578.1705515 1
< 0.1%
-578.1681603 1
< 0.1%
ValueCountFrequency (%)
1819.32839 1
< 0.1%
1814.462681 1
< 0.1%
1813.616776 1
< 0.1%
1813.304793 1
< 0.1%
1813.129766 1
< 0.1%
1812.580032 1
< 0.1%
1812.225191 1
< 0.1%
1811.44257 1
< 0.1%
1811.134272 1
< 0.1%
1811.053516 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct37540
Distinct (%)72.2%
Missing707
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean11.991963
Minimum-1.175
Maximum34.832501
Zeros0
Zeros (%)0.0%
Negative127
Negative (%)0.2%
Memory size411.9 KiB
2023-07-08T17:29:45.062955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.175
5-th percentile4.1424998
Q17.7705883
median11.39
Q315.710526
95-th percentile21.923403
Maximum34.832501
Range36.007501
Interquartile range (IQR)7.9399379

Descriptive statistics

Standard deviation5.5299085
Coefficient of variation (CV)0.46113454
Kurtosis0.1019332
Mean11.991963
Median Absolute Deviation (MAD)3.9549998
Skewness0.52484498
Sum623546.12
Variance30.579888
MonotonicityNot monotonic
2023-07-08T17:29:45.163063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.30000019 83
 
0.2%
10 71
 
0.1%
6.400000095 69
 
0.1%
10.19999981 68
 
0.1%
10.69999981 67
 
0.1%
10.80000019 64
 
0.1%
7.400000095 64
 
0.1%
12 61
 
0.1%
6.599999905 59
 
0.1%
8.5 57
 
0.1%
Other values (37530) 51334
97.4%
(Missing) 707
 
1.3%
ValueCountFrequency (%)
-1.175000042 1
 
< 0.1%
-1.127777808 1
 
< 0.1%
-1.110000026 1
 
< 0.1%
-1.04000001 1
 
< 0.1%
-1.025000006 1
 
< 0.1%
-1.020000005 1
 
< 0.1%
-1 5
< 0.1%
-0.9699999928 1
 
< 0.1%
-0.9649999917 1
 
< 0.1%
-0.9349999845 2
 
< 0.1%
ValueCountFrequency (%)
34.83250103 1
< 0.1%
34.79473756 1
< 0.1%
34.78250084 1
< 0.1%
34.70588258 1
< 0.1%
34.65294109 1
< 0.1%
34.63947397 1
< 0.1%
34.60526296 1
< 0.1%
34.39749966 1
< 0.1%
34.33749943 1
< 0.1%
34.25500011 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41460
Distinct (%)79.7%
Missing707
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean65.897126
Minimum16.37
Maximum84.920001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:45.269739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16.37
5-th percentile42.3395
Q161.536111
median70.335
Q373.209999
95-th percentile76.039005
Maximum84.920001
Range68.550001
Interquartile range (IQR)11.673889

Descriptive statistics

Standard deviation10.729934
Coefficient of variation (CV)0.16282856
Kurtosis2.344302
Mean65.897126
Median Absolute Deviation (MAD)3.6899994
Skewness-1.6067597
Sum3426452.9
Variance115.13149
MonotonicityNot monotonic
2023-07-08T17:29:45.366363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.70000076 15
 
< 0.1%
25.60000038 12
 
< 0.1%
25.79999924 11
 
< 0.1%
25.39999962 9
 
< 0.1%
74.5 9
 
< 0.1%
73.92999992 8
 
< 0.1%
71.6125 8
 
< 0.1%
73.47999916 8
 
< 0.1%
72.56749992 8
 
< 0.1%
73.8375 8
 
< 0.1%
Other values (41450) 51901
98.5%
(Missing) 707
 
1.3%
ValueCountFrequency (%)
16.37000017 1
< 0.1%
16.37894711 1
< 0.1%
16.45749989 1
< 0.1%
16.6300005 1
< 0.1%
16.66500015 1
< 0.1%
16.76749973 1
< 0.1%
16.79999924 1
< 0.1%
16.8149992 1
< 0.1%
16.82249966 1
< 0.1%
16.83235236 1
< 0.1%
ValueCountFrequency (%)
84.92000122 1
< 0.1%
84.56499977 1
< 0.1%
84.0900013 1
< 0.1%
83.86749992 1
< 0.1%
83.77250061 1
< 0.1%
83.45500031 1
< 0.1%
83.28000031 1
< 0.1%
83.12749977 1
< 0.1%
83.02749939 1
< 0.1%
83.025 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51997
Distinct (%)> 99.9%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean73.573775
Minimum3.0450732
Maximum294.16576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:45.471453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.0450732
5-th percentile4.6065016
Q151.426723
median74.573946
Q397.586757
95-th percentile134.05992
Maximum294.16576
Range291.12069
Interquartile range (IQR)46.160035

Descriptive statistics

Standard deviation37.512249
Coefficient of variation (CV)0.50985896
Kurtosis0.093222716
Mean73.573775
Median Absolute Deviation (MAD)23.070033
Skewness0.073169571
Sum3825689.2
Variance1407.1688
MonotonicityNot monotonic
2023-07-08T17:29:45.691186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.56712084 2
 
< 0.1%
4.12093991 1
 
< 0.1%
3.866876864 1
 
< 0.1%
3.810434014 1
 
< 0.1%
4.294169873 1
 
< 0.1%
4.664318264 1
 
< 0.1%
3.736417996 1
 
< 0.1%
4.39868338 1
 
< 0.1%
3.730499035 1
 
< 0.1%
4.029013328 1
 
< 0.1%
Other values (51987) 51987
98.6%
(Missing) 706
 
1.3%
ValueCountFrequency (%)
3.045073158 1
< 0.1%
3.045223489 1
< 0.1%
3.051007221 1
< 0.1%
3.183275461 1
< 0.1%
3.194137442 1
< 0.1%
3.218867042 1
< 0.1%
3.225376093 1
< 0.1%
3.246089518 1
< 0.1%
3.286309695 1
< 0.1%
3.293298292 1
< 0.1%
ValueCountFrequency (%)
294.1657616 1
< 0.1%
259.5270164 1
< 0.1%
255.2621731 1
< 0.1%
253.6352345 1
< 0.1%
250.6668613 1
< 0.1%
247.5150438 1
< 0.1%
245.125164 1
< 0.1%
239.644618 1
< 0.1%
239.0560822 1
< 0.1%
238.5486855 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52155
Distinct (%)99.8%
Missing453
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean6.0359571
Minimum0.23623154
Maximum23.241919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:45.792732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.23623154
5-th percentile2.1981907
Q13.9962415
median5.6549983
Q37.6484994
95-th percentile11.227173
Maximum23.241919
Range23.005687
Interquartile range (IQR)3.6522579

Descriptive statistics

Standard deviation2.8158891
Coefficient of variation (CV)0.46651907
Kurtosis0.90756412
Mean6.0359571
Median Absolute Deviation (MAD)1.7975952
Skewness0.80833535
Sum315384.79
Variance7.9292315
MonotonicityNot monotonic
2023-07-08T17:29:45.893307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.239999771 3
 
< 0.1%
4 3
 
< 0.1%
4.789999962 2
 
< 0.1%
4.099999905 2
 
< 0.1%
3.420000076 2
 
< 0.1%
4.537312889 2
 
< 0.1%
3.529999971 2
 
< 0.1%
2.869999886 2
 
< 0.1%
3.970000029 2
 
< 0.1%
2.019999981 2
 
< 0.1%
Other values (52145) 52229
99.1%
(Missing) 453
 
0.9%
ValueCountFrequency (%)
0.2362315411 1
< 0.1%
0.2772238764 1
< 0.1%
0.2814376201 1
< 0.1%
0.2943158895 1
< 0.1%
0.3248625129 1
< 0.1%
0.3307688288 1
< 0.1%
0.3381119637 1
< 0.1%
0.344831378 1
< 0.1%
0.3520688988 1
< 0.1%
0.3565874428 1
< 0.1%
ValueCountFrequency (%)
23.24191856 1
< 0.1%
22.7251873 1
< 0.1%
22.04384551 1
< 0.1%
21.65763726 1
< 0.1%
21.59448738 1
< 0.1%
21.29743137 1
< 0.1%
21.17227497 1
< 0.1%
21.16103415 1
< 0.1%
21.10829816 1
< 0.1%
20.9045435 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51996
Distinct (%)> 99.9%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean31.840174
Minimum1.3936913
Maximum243.62478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:45.990588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.3936913
5-th percentile4.6131619
Q120.604571
median29.13201
Q340.512854
95-th percentile64.529157
Maximum243.62478
Range242.23109
Interquartile range (IQR)19.908282

Descriptive statistics

Standard deviation18.779349
Coefficient of variation (CV)0.58980046
Kurtosis6.5351656
Mean31.840174
Median Absolute Deviation (MAD)9.7290702
Skewness1.6082257
Sum1655625.3
Variance352.66395
MonotonicityNot monotonic
2023-07-08T17:29:46.095351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.50074352 2
 
< 0.1%
4.329871178 2
 
< 0.1%
31.54417596 1
 
< 0.1%
4.107130921 1
 
< 0.1%
4.838586807 1
 
< 0.1%
3.678266269 1
 
< 0.1%
4.565971363 1
 
< 0.1%
4.16719445 1
 
< 0.1%
4.13629269 1
 
< 0.1%
5.164875733 1
 
< 0.1%
Other values (51986) 51986
98.6%
(Missing) 706
 
1.3%
ValueCountFrequency (%)
1.393691301 1
< 0.1%
2.001062632 1
< 0.1%
2.963482395 1
< 0.1%
2.997283067 1
< 0.1%
3.070137131 1
< 0.1%
3.187265487 1
< 0.1%
3.194287125 1
< 0.1%
3.195783973 1
< 0.1%
3.230147457 1
< 0.1%
3.244938844 1
< 0.1%
ValueCountFrequency (%)
243.6247829 1
< 0.1%
233.8543818 1
< 0.1%
214.1958733 1
< 0.1%
201.1860017 1
< 0.1%
193.9068712 1
< 0.1%
193.1732246 1
< 0.1%
192.8721458 1
< 0.1%
190.8985531 1
< 0.1%
190.3808554 1
< 0.1%
189.8778383 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct40
Distinct (%)0.1%
Missing707
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean619.39375
Minimum594
Maximum636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:29:46.196209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile596
Q1612
median616
Q3633
95-th percentile634
Maximum636
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.638311
Coefficient of variation (CV)0.020404324
Kurtosis-1.0185011
Mean619.39375
Median Absolute Deviation (MAD)12
Skewness-0.30039092
Sum32206617
Variance159.72691
MonotonicityIncreasing
2023-07-08T17:29:46.286154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
613 11506
21.8%
634 8776
16.7%
611 3192
 
6.1%
631 3048
 
5.8%
596 2740
 
5.2%
628 2339
 
4.4%
633 2248
 
4.3%
636 2100
 
4.0%
624 1614
 
3.1%
632 1537
 
2.9%
Other values (30) 12897
24.5%
ValueCountFrequency (%)
594 1473
2.8%
595 55
 
0.1%
596 2740
5.2%
597 6
 
< 0.1%
598 1173
2.2%
599 240
 
0.5%
601 4
 
< 0.1%
602 3
 
< 0.1%
603 21
 
< 0.1%
604 516
 
1.0%
ValueCountFrequency (%)
636 2100
 
4.0%
635 283
 
0.5%
634 8776
16.7%
633 2248
 
4.3%
632 1537
 
2.9%
631 3048
 
5.8%
630 135
 
0.3%
628 2339
 
4.4%
627 898
 
1.7%
626 345
 
0.7%

Interactions

2023-07-08T17:29:41.523592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.450815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.549140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.705219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.860195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.070112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.215832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.403731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.630524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.815603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.983576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.147581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.376216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.605054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.533511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.633100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.788562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.043462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.154506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.301062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.482001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.716684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.900738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.069312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.227309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.458488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.693938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.621329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.725404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.879668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.131832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.246774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.397722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.572874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.811886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.995949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.162149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.318499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.552455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.785894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.709505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.817597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.972205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.221308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.339783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.493046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.661898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.908421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.090055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.256733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.407336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.643741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.867095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.789202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.902035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.055861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.299765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.421145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.580729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.743592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.993268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.174444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.340402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.489674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.727327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.951531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.872730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.991599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.146385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.385202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.506893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.671537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.828703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.085224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.264381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.431369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.574770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.815446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.045573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:27.965240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.087691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.242051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.476300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.604071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.768817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.920495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.183574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.360954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.527712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.785633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.910905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.125574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.043985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.172675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.327637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.558816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.687397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.855489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.001240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.270363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.447596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.611847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.862717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.995990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.217575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.132974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.265906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.421077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.648853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.782010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.953072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.092050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.363800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.542085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.707785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.953471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.088628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.304232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.221267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.358880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.514272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.737659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.872201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.046576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.183867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.458749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.635335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.799676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.041382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.183005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.389667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.306526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.449115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.604043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.824659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:32.963053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.141703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.271761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.551347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.726413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.890728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.130587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.271537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.471699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.387821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.534827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.689298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.905602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.046278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.227670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.352199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.639129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.811799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:38.974600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.210284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.356605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:42.554924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:28.470187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:29.621645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:30.776619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:31.991444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:33.133217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:34.318833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:35.551900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:36.728877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:37.900660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:39.063792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:40.294992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:41.441954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:29:46.369610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0240.014-0.3680.7010.9850.985-0.1750.9400.6090.9780.809-0.086
Wind direction (°)0.0241.0000.9140.0350.0180.0460.047-0.1030.0210.1930.0100.1120.030
Nacelle position (°)0.0140.9141.0000.0450.0040.0350.036-0.0980.0090.179-0.0000.0990.026
blade_angle-0.3680.0350.0451.000-0.490-0.355-0.3560.152-0.413-0.191-0.347-0.173-0.078
Rear bearing temperature (°C)0.7010.0180.004-0.4901.0000.6950.6920.0680.7840.4710.6850.5370.013
Rotor speed (RPM)0.9850.0460.035-0.3550.6951.0001.000-0.1730.9360.6400.9620.824-0.081
Generator RPM (RPM)0.9850.0470.036-0.3560.6921.0001.000-0.1830.9360.6400.9620.824-0.084
Nacelle ambient temperature (°C)-0.175-0.103-0.0980.1520.068-0.173-0.1831.000-0.141-0.139-0.150-0.1340.166
Front bearing temperature (°C)0.9400.0210.009-0.4130.7840.9360.936-0.1411.0000.5690.9220.750-0.070
Tower Acceleration X (mm/ss)0.6090.1930.179-0.1910.4710.6400.640-0.1390.5691.0000.5690.836-0.079
Wind speed (m/s)0.9780.010-0.000-0.3470.6850.9620.962-0.1500.9220.5691.0000.792-0.085
Tower Acceleration y (mm/ss)0.8090.1120.099-0.1730.5370.8240.824-0.1340.7500.8360.7921.000-0.105
Metal particle count counter-0.0860.0300.026-0.0780.013-0.081-0.0840.166-0.070-0.079-0.085-0.1051.000

Missing values

2023-07-08T17:29:42.675588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:29:42.882553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:29:43.121036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02020-01-01 00:00:00281.981552114.42015093.5087740.00000067.0325009.7599131159.1484437.00750068.30750083.1331814.93076431.544176594.0
12020-01-01 00:10:00194.945205115.40067993.5087740.27816466.0550009.5507021134.2243277.09750066.79250198.3378384.14408535.269012594.0
22020-01-01 00:20:00180.241521111.796981105.2712910.32133265.5775009.4532941122.8014587.04500065.72000083.5825344.29900826.754336594.0
32020-01-01 00:30:0091.398078104.672035111.0696870.99333364.1700009.3554751112.2402276.80250063.58500079.1012343.53521628.731633594.0
42020-01-01 00:40:0098.042697112.202212111.0696870.91850063.4525019.3079751107.0167146.77500061.96250069.8754773.55884724.993689594.0
52020-01-01 00:50:00269.167712128.040470111.0696870.07771965.5631589.7161511154.3610166.98157965.03157965.2371054.87264125.384063594.0
62020-01-01 01:00:00351.540451133.889032130.6301250.00000067.84750110.3537361230.1643667.07500068.55000063.2900465.31758823.158722594.0
72020-01-01 01:10:00325.485345128.977217131.9232940.00000067.32000010.1008111200.7950847.00750068.45999958.9088905.71287619.080858594.0
82020-01-01 01:20:00278.374342126.732506131.9232940.00000067.6200009.7575741159.0053467.03500068.91250169.6889165.27938624.862645594.0
92020-01-01 01:30:00250.789034130.382582131.9232940.02466667.5524999.5899471139.8380096.90500068.70250172.0061045.21449227.805728594.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
526942020-12-31 22:20:00213.030355310.428856327.2885740.31733360.9575009.5746961135.4019812.000059.26500069.2209555.01110225.410178636.0
526952020-12-31 22:30:00153.963457312.012738327.2885740.49333361.4950009.3017001105.5557611.992560.79750077.1499264.56111026.589498636.0
526962020-12-31 22:40:00133.967656299.363143303.2644180.67033360.8225009.2447481098.5156821.942559.89500073.4804944.67189123.887428636.0
526972020-12-31 22:50:0084.517547297.933354286.6786801.19700059.9825009.3375351109.5538412.030058.67249999.2612173.58060229.819483636.0
526982020-12-31 23:00:0068.172284300.421887286.6786801.31833359.6450009.3828721115.1316912.165057.76000089.1046723.14390024.174692636.0
526992020-12-31 23:10:0064.335619297.870852286.6786801.29333359.5700019.3705191114.1478652.345057.39000071.5435023.39739023.282889636.0
527002020-12-31 23:20:0036.255348305.147340286.6786801.44333358.9200009.2155681094.8473762.367556.59500077.7792183.02487827.599043636.0
527012020-12-31 23:30:0094.631310297.075575286.6786800.88600059.5525019.1439001086.0149402.370057.00750093.4164743.83437025.812826636.0
527022020-12-31 23:40:00112.857855295.999670286.6786800.66800060.3450029.1794971090.3565772.457558.13499977.2001104.21174524.626179636.0
527032020-12-31 23:50:0099.349321302.114189286.6786800.96650060.0925009.2549031098.9798552.357557.94500096.5739643.97373733.298340636.0